Soochow University

Zaigang Chen

Jun Wang

Zhongkui Zhu

Weiguo Huang

Changqing Shen

Juanjuan Shi

Xingxing Jiang

Dong Wang

Xu Wang

Aiwen Zhang

Guifu Du

Yongjun Shen

Shuangjie Liu

The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the deep convolutional neural network model.

Dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions

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Suzhou, China

Zaigang Chen